Summary Impact Type

Research Subject Area(s)

Download original

Summary of the impact

In the UK and the rest of Europe, public bodies and policymakers have
struggled to quantify
migration and make accurate population forecasts because of inconsistent
data from a variety of
disparate sources. The University of Southampton has demonstrated how, via
the use of statistical
modelling, conceptual frameworks and migration modelling, policymakers can
radically improve the
information they glean from the data sources at their disposal. The team
successfully guided the
Office for National Statistics (ONS) in overhauling their methodologies,
finally making them
compliant with European Union (EU) regulations. The wider impact of
accurate numbers is
significant for public service provision, planning, and the UK economy.
Furthermore, ONS data
feeds into numerous areas of public policy, hence the provision of
accurate estimates by
Southampton researchers has significant reach and impact on such policies.

Underpinning research

Accurate information about the size and movements of populations is
essential for
policymakers and government bodies, from the local to the international
level. Whether used to
inform decisions on funding for infrastructure and amenities, or for
economic forecasting, accurate
population data is crucial. But in the European Union, the study of
international migration, which is
so important to the understanding of population change, has been hindered
by problems with data
availability, quality and consistency. A study by Poulain et al. in 2006
showed that harmonisation of
data collection processes and the data they generate was not even close to
being realised.

The University of Southampton's Statistical Sciences Research Institute
and the Economic
and Social Research Council's Centre for Population Change forged a
collaboration to tackle this
problem. A team led by James Raymer, Professor of Demography (2004 to
present), Jakub Bijak,
Lecturer in Demography (2009 to present), and Peter W. F. Smith, Professor
of Social Statistics
(1990 to present), have researched the accuracy and analysis of migration
data and worked with
government bodies to improve their understanding of population dynamics.
During the REF impact
period, they have used that knowledge to work directly with those
government bodies to
significantly improve their use and understanding of the statistics
available to them.

Raymer and Bijak completed a three-year Migration Modelling for
Statistical Analyses
(`MIMOSA') project on estimating international migration flows and stocks
in Europe in 2009 [G1].
The MIMOSA project was funded by Eurostat, the statistical office of the
European Union, which
was unable to reconcile migration data provided by member states. Bijak
joined Southampton for
the last year of the project but was involved in it from the beginning at
CEFMR, Warsaw.

The team developed a modelling approach to estimate international
migration flows
between countries in Europe. For the first time, a methodology was
developed for obtaining reliable
and consistent estimates of international migration between European
countries, and to estimate
the missing data. In particular, a categorical data analysis approach [3.1,
3.2] was applied to the
structures in the migration flow tables, representing the gross flows of
immigration and emigration
and the associations between countries. The work has been subsequently
extended in a project on
Integrated Modelling of European Migration (`IMEM') [G2], which
uses the Bayesian statistical
approach to analyse the uncertainty of the migration estimates, while
accounting for the many
differences in definitions, quality and sources of available migration
data. In parallel, innovative
methodologies for migration forecasting have been developed [3.3].

The MIMOSA estimates [3.4] provided valuable insights into the
overall picture of
population movements, as well as suggesting areas for further improvement
in the modelling
approach. EU regulations state that a breakdown of `scientifically based'
statistics on migration
must be submitted to Eurostat by each member state. Failure to meet the
requirements has the
potential to result in a hefty fine. The Southampton team advised the UK's
Office of National
Statistics on how to improve their methodology and suggested
methodological improvements to be
implemented by the ONS, like pooling data and estimating age distributions
over time. The ONS
subsequently commissioned the Southampton team to work on five projects.
In the first one Bijak
has performed an independent review of methods for distributing
international immigration
estimates to sub-national regions. In the second project Raymer and other
co-authors delivered
recommendations for how the ONS could improve the migration statistics
sent to Eurostat to fulfil
its legal obligation of quality data reporting. The third project, by
Smith, Raymer and Bijak,
assessed uncertainty in the ONS mid-year population estimates. The fourth
one, by Raymer and
co-authors, put together the first ever conceptual framework for UK
population and migration
statistics. Recently, Bijak was also commissioned by the ONS to overhaul
the methodology behind
the international migration assumptions in population projections.

[3.4] Raymer J, de Beer J and van der Erf R (2011) Putting the
pieces of the puzzle together: Age
and sex-specific estimates of migration amongst countries in the EU /
EFTA, 2002-2007.
European Journal of Population 27(2): 185-215.

Details of the impact

The University of Southampton's research on statistical modelling for
migration, and on
producing accurate population estimates and projections, has improved the
daily working methods
of public bodies that rely on population statistics in their forecasts and
decision-making, ultimately
affecting the users of public services all across the UK. The team has
shown how data limitations
can be overcome by using statistical modelling to produce more reliable
and detailed migration
estimates and forecasts, with associated measures of uncertainty. They
demonstrated, for
example, how administrative sources like student registers, NHS registers
and National Insurance
data, can be used to supplement traditional survey data used by the ONS.

In the UK, this work has become an integral part of ONS operations. The
final report
of the Migration Statistics Improvement Programme (ONS 2012a [5.1])
mentions four areas of
improvement which resulted directly from the team's work and
recommendations:

Demographic models for migration and population estimates;

Long-term international immigration estimates;

Measures of uncertainty in population statistics (reported in detail
in ONS 2012b [5.2]); and

Conceptual frameworks.

These methodological changes are of national significance and
increase the national
capability to accurately estimate population at a variety of
geographic levels. The work on
measuring uncertainty in population estimates has fed into professional
guidelines of the ONS and
its customers, providing the users of public statistics "with better
information regarding the
uncertainty associated with the local authority mid-year estimates"
(ONS 2012b [5.2], p.1). Here,
the reach and significance of the research impact go far beyond the
methodology of official
statistics, indirectly affecting the national economy and public
sphere. Good quality population
estimates and projections are crucial for transparent and fair allocation
of public funds in such
areas as social services, education, police, fire and rescue, highway
maintenance, or environment
and culture, where they are explicitly used in many of the allocation
formulae (CLG 2013 [5.3]).
The ONS estimates also serve as benchmarks for calculating standardised
mortality rates, used in
allocating funds to Clinical Commissioning Groups of the National Health
Service (NHS 2013
[5.4]).

To quote the ONS in regard to the conceptual framework developed by
Southampton's
researchers, "[it has benefited] users of population statistics,
including analysts, statisticians,
researchers as well as policy and decision makers), [...] data providers
to understand the context
and uses to which their data are put, [and] producers of population
statistics to provide a basis on
which to prioritise future developments in population statistics, and
communicate latest data and
put data in proper context" (ONS 2012a [5.1], p. 6). The
framework gives details of the component
parts of the population statistics model, together with the data sources
used to estimate these
parts. In particular, it examines how individual sources such as the
Census, surveys and
administrative data differ in terms of quality and coverage.

The ONS states that, in relation to Southampton's guidelines and
recommendations which
were implemented to change the current practice in order to meet the
Eurostat requirements for
statistics on international migration, following the MIMOSA project, "for
the first time since the
regulation was introduced, the UK was considered compliant with the
requirements under Article 3
of Regulation 862/2007." [U1] This meant that the UK could
fulfil its statistical requirements under
EU law, avoiding a potentially significant fine.

Southampton's current work on future migration assumptions has also been
found
"...important to ensure the ongoing relevance and accuracy of the
projections which are used
widely across government forplanning and policy making"
[U2] in areas such as fiscal
sustainability, pensions, health and education. The wider societal impact
is significant, even
notwithstanding the issue of direct allocation of government funds.
Population projections form the
base of forecasts for the economy and public finance prepared by the
Office for Budgetary
Responsibility, analyses of future workforce and pension provision by the
Department for Work and
Pensions, actuarial analysis for the public sector carried out by the
Government Actuary's
Department, or various types of macroeconomic studies by the Bank of
England.

The ONS has described its collaboration with the University of
Southampton as having
made an invaluable contribution to the development of more accurate
population and migration
estimates [U3]. The Southampton team has put the ONS "...in an
excellent position to think about
the gaps and the priorities for the future." [U4] As part of
a Beyond 2011 programme, the ONS is
working on alternatives to the national Census, including dropping it
altogether in favour of
Southampton's modelling approach to the various data streams to which they
have access.

Southampton's research was already highlighted in a 2009 UK Statistics
Authority report
entitled `Migration Statistics: The way ahead?' In Part 2 of the
report, it stated: "There is a strong
case for constructing synthetic estimates of migration statistics [...]
Raymer and colleagues at the
University of Southampton have been applying the methods to current UK
and European migration
data. A shared official and academic programme of such synthetic
estimates should be
considered." (UK Statistics Authority 2009 [5.5], p. 105).
The current case study presents selected
outcomes of the implementation of this programme in practice within the
REF impact period.

Currently, the Southampton team is looking into impacts of their work on
modelling
migration in Europe, carried out in 2009-2012 in the IMEM project. In
terms of impact, the research
is directly targeted at the EU-level public statistics (Eurostat) and
decision-making (European
Commission), fulfilling the requirements for harmonised migration
statistics stipulated by recent EU
law. During the latest meeting of the Eurostat Working Group on Migration
Statistics in 2012, it
was explicitly stated that:

"the research project `Integrated Modelling of European Migration'
(IMEM) by the
University of Southampton could be used as a starting point to develop a
model of intra-EU
migration. This might provide a useful benchmark to assess the accuracy
of reported
migration flows. Eurostat reported that it was following this project
with interest, noting
that some aspects of the IMEM project built upon the earlier MIMOSA
project that was
funded by Eurostat." (Eurostat 2012 [5.6], p. 3)